Chad Gordon
December 7th: Updated predictions moving into the Sweet Sixteen are live To track changes in predictions, we added a second sheet. This sheet highlights the change in predicted likelihood of making the next round, as compared to our pre-tournament predictions. For example, the model doesn't love Pitt and only gave them a 47% chance of [...]
Interactive Dashboard: https://public.tableau.com/views/Expectationvs_Reality/Overall?:language=en&:display_count=y&publish=yes&:origin=viz_share_link This is the direction I think the sport of volleyball needs to go - getting all skills, all contacts, all situations, and all performance evaluation into the single language of points. The same way the NBA has expected points per shot based on the location on the floor + number of defenders, [...]
% split of Terminal vs. Non-Terminal attacks 1. Welcome to attacking: the most important skill in our game. I'd also argue it's the most interesting, least understood, and most dynamic of our game - making it the best place to pick some low-hanging fruit. 2. We need to understand the reality of attacking. As you [...]
Motivation While trying to adjust our expected points (PWP) model to account for team and opponent strength, we ran into a few issues. As far as we could tell, the only analytics-based rating system out there was the Pablo rankings, and we wanted ratings that (1) are not paywalled, (2) can be reproduced by other [...]
This marks our first public unveiling of our new rating system [link to the rating system explainer post]. Our goal will be to update these rankings every Monday evening / Tuesday morning. First and foremost, huge thanks to Professor Dwight Wynne for doing the heavy lifting and getting this system off the ground. I'm excited [...]
Dwight Wynne teaches statistics at California State University, Fullerton and supervises student projects helping their women's volleyball team get more out of their data. Introduction One tool that has become increasingly common in the modern consumption of sports analytics is a win probability graph. A win probability graph shows, given the current score and time [...]
Introduction Having a lead in a set conveys confidence that we are more likely to win. We should be even more confident if that lead is near the end of the set, unless we are matched against a much better opponent. Using a lot of data and some modeling, we can quantify that confidence into [...]
Madeline Gates - 2019 Stanford University Interactive Dashboard: https://public.tableau.com/views/SeasonTrends/HittingEfficiency?:language=en&:display_count=y&publish=yes&:origin=viz_share_link Is your team getting better or worse as the season progresses? Are your players developing and performing stronger as the year goes on? In the chart above, we take a look at Madeline Gates, the middle from Stanford. We're looking at a rolling average of her [...]
Interactive Dashboard: https://public.tableau.com/profile/chad.gordon#!/vizhome/Blocking-TheUnsungHero/BlockingAffectsHittingEff In my opinion, blocking is the most "shrugged off" skills in our sport. Guy gets up, bounces a ball in the middle of the court and blocker turns to the coach who promptly gives him the "thumbs up, move on" type of gesture. While you're unlikely to fix your team in the [...]
Interactive Dashboard: https://public.tableau.com/profile/chad.gordon#!/vizhome/ServiceErrors/Service_Errors I'd argue, no. At least not in the way that most people think? The core issue here is that given a specific input, we see a wide range of outputs. As you can see from the rainbow..ish blob on the right, at any given range of Service Error Percentage, there's quite a [...]
So, admittedly it's been a while since I posted about Point Win Probability (PWP), which to be honest, I think is pretty sweet. But today, I'm mostly here to steal a good idea from Nate Ngo (whose blog is here and you should 1000% check out if you have not). His issue was that, like [...]
Rethinking Volley 